Three-dimensional image processing to locate nanoparticles in biological and nonbiological media

ABSTRACT

Disclosed are various embodiments for methods and systems for three-dimensional imaging of subject particles in media through use of dark-field microscopy. Some examples, among others, include a method for obtaining a three-dimensional (3D) volume image of a sample, a method for determining a 3D location of at least one subject particle within a sample, a method for determining at least one spatial correlation between a location of at least one subject particle and a location of at least one cell structure within a cell and/or other similar biological or nonbiological structure, a method of displaying a location of at least one subject particle, method for increasing the dynamic range of a 3D image acquired from samples containing weak and strong sources of light, and method for sharpening a 3D image in a vertical direction.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of co-pending U.S. patentapplication having Ser. No. 14/775,309, filed Sep. 11, 2015, which isthe 35 U.S.C. § 371 national stage application of PCT Application No.PCT/US2014/024346, filed Mar. 12, 2014, which claims priority to and thebenefit of, U.S. provisional application entitled “THREE-DIMENSIONALIMAGE PROCESSING TO LOCATE NANOPARTICLES IN BIOLOGICAL AND NONBIOLOGICALMEDIA” having Ser. No. 61/776,977, filed Mar. 12, 2013, all of which arehereby incorporated by reference in their entireties.

BACKGROUND

The introduction of drugs and chemical agents into living cells hasrecently begun to utilize nano-scale objects of less than 100 nanometerdimensions and/or microscopic objects, herein known as the subjectparticle, in various configurations as carriers. For example,therapeutic drugs can be coated onto, or encased within, nano-sizedparticles such as gold and silver. The functionalized subject particleis then introduced into the body, where it is absorbed into tissues andultimately taken up by cells. The ability to target just the cells thatshould receive the drug is enabled by functional coatings on theparticles, which are recognized by the cell surface. Research into theprocess of cell uptake of subject particles, and the intracellularprocessing of the drug-particle, is important for development of thedrug therapy process. The cell-subject particle interactions that areinvolved in uptake and distribution within cells are elucidated by manydifferent types of studies using diverse techniques.

One of the most widely used tools is cell imaging by fluorescentmicroscopy. Conventional fluorescent microscopy does not allow the threedimensional volume of the cell to be viewed. A confocal fluorescencemicroscope is therefore used to image thin sections of the cell over avolume to view the cell structure in three dimensions. However thesemethods require introduction of fluorescent labels. The attachment offluorophores to subject particles or cell structures can alter theintended function for drug delivery and significantly increases thedifficulty of the cell preparation. Systems and methods that permitdetermination of the location of subject particles in three dimensionswithout altering the intended function for drug delivery are thereforeof interest and have the potential to play an important role inincreasing the understanding of nano-drug delivery, thus furthering thedevelopment of nanomedicine.

SUMMARY

The present disclosure provides wide-field microscopy methods that candetermine the locations of subject particles within unstained andfluorescing cells or in semi-transparent nonbiological media and othermedia through which light can be transmitted, for example fibermatrices. The present disclosure provides novel methods for acquisitionof image data from functionalized subject particles within unstained andfluorescent cell preparations. Also provided are methods for producingthree dimensional cell-subject particle images using broadbandillumination scattered from the cell volume, as opposed to currentmethods requiring fluorescent cells and particles. Such an image can beacquired with a dark-field illumination method and the use of imagesectioning techniques rather than by conventional fluorescence methodsusing filter cubes designed for specific fluorophores. Nonlimitingexamples of suitable systems and methods of illumination are taught inU.S. Pat. No. 7,564,623, “Microscope Illumination Device and AdapterTherefor,” and U.S. Pat. No. 7,542,203, “Microscope Illumination Deviceand Adapter Therefor,” both of which are incorporated by referenceherein in their entirety.

In addition, the present disclosure also provides novel computationalmethods to perform three-dimensional deconvolution of dark-field imagesections and thereby reveal locations of subject particles in relationto the cell structure. The new methods employ multiple point spreadfunctions (PSF) to correct the image focus across a wide spectral range,as opposed to single PSFs that are designed for specific fluorophorewavelengths. The new PSFs can be designed to work with variable andfixed iris objectives used for dark-field microscopy. The newdeconvolution methods automatically detect cell structure and subjectparticles in images through use of separate PSFs for each cell structureand type of subject particle of interest to optimize thethree-dimensional image reconstruction. As an example, the subjectparticle may be reflective over a narrow range of wavelengths whereasthe cell image is produced over a wide range of wavelengths or over adifferent range of wavelengths.

A unique feature of the deconvolution is the conversion of thethree-dimensional image of all of the subject particles into sphericalicons, which are located precisely at the subject particle coordinatesin the three dimensional volume. A data set comprised of all of thesubject particle positions results from this method, and with it a usercan then examine a particular subject particle of interest by moving amicroscope stage along three dimensions to examine the location of thatsubject particle. For example, a user can examine a particular subjectparticle though use of hyperspectral microscopy to evaluate spectralproperties that report whether the drug is attached or detached from theparticle.

The methods of the present disclosure are different and unique in thatthey are able to operate with standard research wide-field microscopesrather than confocal microscopes. The elimination of the need forfluorescent labeling reduces complexity of the functionalized subjectparticle, which in many cases can alter the function. The broad spectralrange over which the three-dimensional image can be rendered allows themethods to be used with diverse subject particle configurations that canbe observed anywhere in the visible to near infrared wavelength range.No state-of-the-art method(s) in the field of optical microscopy is/areknown to exist that can automatically determine subject particlepositions within cells.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings,including any drawings incorporated herein by reference, are notnecessarily to scale, with emphasis instead being placed upon clearlyillustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a flowchart illustrating a method of image formation accordingto various embodiments of the present disclosure.

FIG. 2 is a flowchart illustrating a method of subject particle locationaccording to various embodiments of the present disclosure.

FIG. 3 is a flowchart illustrating a method of image formation accordingto various embodiments of the present disclosure.

FIG. 4 is a schematic block diagram of a computing device according tovarious embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following discussion, a general description of the system and itscomponents is provided, followed by a discussion of the operation of thesame. Herein are described embodiments of methods and systems foracquisition and computational processing of three-dimensional image dataof subject particles in media through the use of dark-field microscopy.Objects that can be subject particles include functionalized metallicand non-metallic nanoparticles and nanorods, single and multi-walledcarbon nanotubes, gold shells, quantum dots and nanofilaments. Althoughthe illustrative embodiments discussed herein may refer to cellularmedia, the methods and systems of the present disclosure are not limitedto biological media and can be used with any suitable transparent,semi-transparent, or translucent medium. Furthermore, although imagingof nanoparticles in cells represents one possible application of themethods and systems of the present disclosure, subject particles in thepresent disclosure need not be limited to nano-sized particles, but canbe any size particles that can be imaged using the methods and systemsof the present disclosure. Furthermore, media of the present disclosureneed not be limited to biological materials such as cells, but cancomprise such non-limiting examples as fiber matrices, filter matrices,emulsions, and any suitable transparent, semi-transparent, ortranslucent material that permits images to be obtained using themethods and systems of the present disclosure.

Referring now to the drawings, one or more preferred embodiments of thepresent disclosure are described.

Disclosed Methodologies

The methods and systems disclosed herein are directed to two generalcategories. Acquisition methods used to obtain three-dimensional (3D)image data of biological and nonbiological media containing subjectparticles using dark-field microscopy are described. In addition,methods and systems are described that extend a 3D deconvolution processto image data not previously amenable to it.

Acquisition Methodologies

This section presents the methods and systems for acquisition 3D imagedata. A recording, using a dark-field imaging method, is made of adefined volume containing some media which can be interspersed withsubject particles. The recording can be made in conjunction with anysuitable dark-field microscopy system, such as, by way of non-limitingexample, the system described in U.S. Pat. No. 7,564,623, “MicroscopeIllumination Device and Adapter Therefor.” Media can be biological, suchas cellular material, or nonbiological, such as a fiber matrix.Recordings of media without subject particles can also be obtained usingthe same technique. When subject particles are included, they do notneed to be labeled fluorescently to be detected and localized with theprocess described in this disclosure. The media and cells can besupported by a glass slide and be stabilized under a cover slip, or theycan be in an environmental flow chamber that can support live cells.

The recording consists of a stack of 2D images that are obtained withfixed distances in the vertical (or focus) direction between each image.In a live cell preparation, the cells and positions of subject particlesshould be stable in a temporal and spatial manner for the severalseconds that are needed to acquire the stack of images. The image stackis recorded by alternately taking images and then moving the microscopestage to a new predetermined position along the vertical direction. Theresulting image stack represents a low resolution representation of thevolume, and is the input for the computational process. When bothbiological media and subject particles are unstained (non-fluorescent),the process allows for the use of broadband illumination which reflectsindiscriminately from the cell and the subject particle. If the subjectparticle has a resonance peak at a specific wavelength, and theunstained cell has a different reflectance spectrum, the process allowsfor two spectra, one emphasizing the subject particle peak wavelengthand the other characteristic of the cell reflectivity, the illumination,and the camera sensitivity, taken together. The method accounts for bothspectra simultaneously, so that both types of objects are efficientlyimaged.

Alternatively, the process allows the first narrowband wavelength to beshifted off the subject particle peak so that its contribution to theimage is reduced. This second case is useful because the lightreflecting from many of the subject particles used in cell studies is atleast one magnitude stronger than the cell reflection. If highestefficiencies were used, the image would saturate at the locations ofsubject particles when the cell structure was at a reasonable value inthe image. The present disclosure also describes a method for increasingthe dynamic range of the recording of both types of objects using tworecordings, each obtained with a high dynamic range camera, made at thesame focus position, where the exposure time is set short in one imageto capture the subject particles without saturation and made long in theother image to capture the cell structure. The process then replaces thesaturated pixels in the long exposure image with the values of the samepixels in the short exposure image, where the values have been scaled toreflect a common exposure time and the data are now present in afloating point format. It is also possible to represent the new imagesin a fixed point format with more than 16 bits.

In some embodiments, the process also allows the cell structure to befluorescently labeled. When the cell emission occurs at one wavelength,and the resonance peak of the subject particle is at a differentwavelength, the computational process can take advantage of the separatewavelengths, as described below, by use of separate point spreadfunctions for each. This type of acquisition can be performed with acontinuum spectrum type of light source, such as a quartz halogensource.

When the cell fluorescence is instead excited by the strong peak of anarc lamp, such as a mercury arc, there is the additional advantage thatthe signal from the cell structure can be raised relative to the signalfrom unlabeled subject particles. To accomplish this, a controlledmixture of narrowband with a wideband light is needed, the first for thecell structure and the second for the subject particle, so that relativesignal strength from each object can be adjusted. The Dual ModeFluorescence Module developed and manufactured by Cytoviva is anon-limiting example of a device for the mixing of light from twospectral regions. The device contains a fluorescence excitation filterin a rotatable wheel that passes the narrowband wavelengths. The deviceallows the filter to be moved slightly to one side to allow a portion ofthe unfiltered light source to also pass into the illumination beam.When the purpose is to image fluorescent objects, a multi-pass emissionfilter is added to the microscope. The narrow band of wavelengthsemitting from the object passes through one channel of the multi-passemission filter. The direct light coming out of the Dual ModeFluorescence Module passes through the other channels of the multi-passfilter and is used to image the unstained objects. In practice thedirect light from the arc lamp which passes through the remainingemission filter bands is away from the peaks of the arc lamp spectrum,and thus by this method the subject particle is illuminated lessstrongly than the cell structure. Since the amount of mixing can beproportionately controlled, the signal strengths from labeled cellstructure and unlabeled subject particles can be equalized before thedata is input into the computational process of 3D deconvolution. Thislast process can materially improve the 3D deconvolution by eliminatingsaturated pixels in the input data while maintaining high pixel valuesfor the cell structure.

Extensions to 3D Deconvolution for Imaging Biological/NonbiologicalMedia Containing Subject Particles Using Dark-Field Microscopy

This section discusses systems and methods of 3D deconvolution processesfor computation of 3D imagery from data acquired as discussed above. Indark-field microscopy light is scattered from a source by objects. Thestrength of the scattering, and thus the image of the object, isrelatively bright against a dark background in the recording made by thecamera. In fluorescence the light is emitted by a label of the object.The strength of the fluorescence emission is relatively weak and therecorded fluorescence is contained within a narrow band of wavelengths.As opposed to the low light conditions in fluorescence, the lightscattered from objects is contained in a broader band of wavelengths andis usually much stronger than the fluorescence. In unstained biologicalmedia such as cells containing subject particles this means there may beweak signal objects (cells) and strong signal objects (subjectparticles) which must both be processed in the deconvolution algorithm.

Next the applicable steps to create the processed 3D image fromdark-field microscopy data are described. As shown in the flowchart ofFIG. 1, image stacks, obtained as described above, are obtained and theninput to a 3D deconvolution and display process, here known as thecomputation. By this approach 3D images of subject particles inbiological and nonbiological media, where both are unstained, isobtainable by dark-field microscopy. 3D deconvolution has beenpreviously described as a method for deblurring (sharpening) the imageof fluorescent objects that are contained in an image stack. The presentdisclosure applies the method for the same purpose with non-fluorescentmaterials that are imaged with dark-field illumination.

The difference between fluorescence and dark-field reflected light isthat the light originates directly from the fluorescent object over anarrow band of wavelengths whereas in dark-field reflected light, theobjects are observed by light that originates from a light source thatcovers a broad range of wavelengths. When a laser is used forfluorescence excitation there can be coherent interference in theillumination bathing the sample, whereas in dark-field reflection, theuse of incoherent light produces a more homogeneous illumination of thesample. The direct fluorescence emission from an object and lightreflected from objects will in general possess different opticalproperties such as coherency and state of polarization. Fluorescencewill be emitted only where fluorescent label is present, whereasdark-field light reflections are obtained from all material surfaces,and as such, the point sources of light differ. In some embodiments, theaddition of broadband light adds a capability to 3D deconvolutionmethods where narrowband or laser light is not required or available.

In order to perform 3D deconvolution with broadband light, a new methodhas been developed. As further shown in the flowchart of FIG. 1, aunique multiple-point spread function (multiple-PSF) designed for thefull range of wavelengths contained by the broadband light can becreated from a combination of narrowband PSFs, either computed ormeasured, that cover the range of broadband wavelengths. The weightedintegral described herein can be achieved in practice by summingindividual PSFs made at a plurality of wavelengths. Several discretewavelengths are picked within the spectral range of the broadband light.The exact number may depend on how much the point spread functionchanges with wavelength. For each center wavelength of a PSF, thespacing between circular rings in the PSF changes. In some embodiments aspacing between center wavelengths of every 50 nm to 100 nm is used tocreate the set of PSFs, which gives a good tradeoff in speed andaccuracy of the deconvolution. A point spread function (PSF) can becalculated or measured for each wavelength. The PSFs are then summedtogether to obtain a multiple-PSF to allow deconvolution of the imagesformed by the broad band of light. If the strength of the light changesat different wavelengths, the different PSFs are weighted by thestrength of the light at the center wavelength of the PSF beforesumming.

As further shown in the flow chart of FIG. 1, the image data is thendeconvolved with the multiple-PSF as described herein to obtain thelocations of the subject particles and formulate a 3D image. The processto apply a multi-psf is to first determine subject particle locationsusing a threshold and the spline peak finder described elsewhere in thisdocument. Using a predetermined radius, the voxels in the input,non-interpolated, grid are classified as subject particle voxels ornon-subject particle voxels according to whether they are located withinthe classification radius of a subject particle. During thedeconvolution algorithm, the subject particle PSF is applied to thesubject particle voxels, and the non-subject particle PSF is applied tothe non-subject particle voxels. This, of course, breaks thetranslation-invariant assumption underlying the FFT-based high speeddeconvolution. This problem is addressed by performing two FFTconvolutions at each iteration: one using the subject particle sourcereconstruction and the other applying the non-subject particle sourcedistribution. The results of the two convolutions are summed to producethe model used for the update at each stage. The update is applied tothe subject particle or non-subject particle source according to thevoxel location. In some embodiments, images that are contained in astack of images, taken through a volume of the sample, are first blurred(made less sharp) using a low pass filter. This step reduces noise forthe next step. The process then finds the brightest points in each ofthe blurred images, which are estimates of the exact location of subjectparticles, as subject particles are generally assumed to produce thebrightest and most point-like parts of the dark-field images. Theprocess then assumes, in one embodiment, a cubic spline form for thelight intensity along the Z axis (which is the direction of focus). Thez-value at which the spline reaches its maximum is determined, and thisis taken as an estimate of the z value of the subject particle. This zvalue is, in general, in between the z values of the input image slices.The x and y values of the subject particle location are determined asthe location of the locally brightest pixel in the smoothed images.Cubic splines are also used, in a later stage in the processing, tointerpolate the deconvolved cell results to an isotropic grid.

In some embodiments, a separate set of PSFs for each type of object, onebeing the biological/nonbiological media and the other the subjectparticle, are optimal for the process described in the presentdisclosure. Some embodiments use a method whereby the subject particlesare processed with one PSF and the rest of the biological ornonbiological media in the recording with at least one other PSF. Therationale is that each component is at a different wavelength (or rangeof wavelengths) and also that the 3D deconvolution process can be doneseparately for nanomaterials and cells or other objects and thenrecombined.

In some embodiments a dark-field imaging method is used specifically toilluminate and excite fluorescence label in biological or nonbiologicalmedia. Use of a dark-field illumination method in the context offluorescence imaging, while not common practice, has been shown to beeffective herein. In some embodiments, 3D image deconvolution isapplicable when the subject particles are coated with functionalchemistry which in turn alters the optical reflection or resonanceproperty of the subject particle. This is possible because theacquisition and computational methods described herein are adapted forwideband imaging.

In some embodiments, 3D images of subject particles in biological orother media, where both are unstained, is obtainable by dark fieldmicroscopy. In some embodiments, the methods and systems describedherein may be applied to only stained biological and nonbiologicalmedia. Although 3D imaging of fluorescently stained cells is known, insome embodiments determining the locations of unstained subjectparticles with respect to specific cell structure in fluorescingbiological media, which express the specific cell structure by thefluorescent label, is obtainable by dark-field microscopy.

Methods for determining spatial relationships between subject particlesand biological or nonbiological structures are also disclosed herein. Asillustrated in the flow chart of FIG. 2, in some embodiments, a vectordescription of the subject particles contained in the 3D image isdetermined, where the x, y, and z coordinates are assigned to specificvolume sectors (cubes) of the image, and cell structures overlapping thespecific volume sectors are also determined, so that a quantitativedescription of the subject particle distribution in relation to cellstructure is revealed. Some embodiments broaden and simplify thedefinition of the spatial relationship to be a binary outcome where thesubject particle is either inside or outside the cell, or the biologicalor nonbiological object of interest. Some embodiments sharpen thedeconvolved 3D image in the vertical direction by applying a peakisolation step to the vertical cubic spline function for each transversepixel. For each vertical cubic spline function, this step starts byidentifying all local minima in the function. The function between onelocal minimum and the next is a peak. The center of mass of this peak isdetermined and the peak is replaced by a function consisting of allzeros except for a value equal to the integrated area of the peak placedat the center of mass.

In some embodiments, a density function of subject particles, or inother words the concentration of subject particles in different parts ofthe cell interior, can be determined. This method uses the idea of the3D cell sector described above. In some embodiments, this methodologycan also be applied to stained cells. Some embodiments use stainedmedia, where the subject particle can be located in relation to one ormore specific cell structures revealed by fluorescent antibody labels tothese structures, where the structures define the intracellular andextracellular space (cell plasma membrane), or the structures define thespace interior or exterior to intracellular organelles (lysosome,nucleus etc). In some embodiments, the density function (concentrationof subject particles) can also be enclosed by an intracellular organelleas well as the cell boundary.

Some embodiments employ user interactive methods where a specific cellstructure can be selected by a user viewing the 3D volume display image,and the shortest distance between individual subject particles and thecell structure can be determined, and where line segments representingthe minimum distance vector can be added for visual effect when aparticular subject particle is subsequently selected. All of theforgoing novel methods are applicable to the study of subject particletransport in biological media.

In some embodiments, the presence of subject particles within the 3Dimage can be artificially marked as an aid in visualizing the particlesin the presence of biological or other structures. In some embodiments,the location of a subject particle is displayed with spherical objects,or icons, where the center of the icon is at the x,y and z coordinate ofthe original subject particle, as determined by 3D computations, and thecolor of the icon is easily distinguishable from color of cell structurein the 3D image. In some embodiments, the icon and cell structure aredisplayed by solid and semi-transparent colors respectively, tofacilitate the viewing of the subject particle icons within cellstructure as the 3D image is rotated.

As shown in the flowchart of FIG. 3, some embodiments comprise a methodfor acquisition and image computation which increases the range ofvalues that can be contained in an image input into the full 3Ddeconvolution process, so that structure of the weaker signals frombiological or other structures is faithfully rendered, while thesignificantly stronger signals that can come from subject particles isalso kept within the full scale permitted for the image. By this method,at each plane of the sample, a set of images is recorded using differentexposure times, one short and one long, where the short exposurecaptures the variation in light intensity of subject particles withoutcausing image saturation, and where the long exposure captures the moresubtle intensity variations caused by cell structure, and where a singleimage is created by identifying saturated pixels in the long exposureimage and replacing their values with the values from identical pixelsin the short exposure image, and rescaling the final image values toreflect a common exposure times.

With reference now to FIG. 4, shown is a schematic block diagram of acomputing device 400 according to an embodiment of the presentdisclosure. The computing device 400 includes at least one processorcircuit, for example, having a processor 403 and a memory 406, both ofwhich are coupled to a local interface 409. To this end, the computingdevice 400 may comprise, for example, at least one server computer orlike device. The local interface 409 may comprise, for example, a databus with an accompanying address/control bus or other bus structure ascan be appreciated.

Stored in the memory 406 are both data and several components that areexecutable by the processor 403. In particular, stored in the memory 406and executable by the processor 403 are an image acquisition application412, an image processing application 415, and potentially otherapplications 418. The image acquisition application 412 and/or the imageprocessing application 415 can implement, when executed by the computingdevice 400, various aspects of the computational processing as describedabove with respect to the flowcharts of FIGS. 1-3. For example, theimage acquisition application 412 can facilitate acquisition and/orstorage of recordings of images and the image processing application 415can facilitate processing of the images. In some implementations, theimage acquisition application 412 and image processing application 415may be combined in a single application. Also stored in the memory 406may be a data store 421 including, e.g., recordings, images, video andother data. In addition, an operating system may be stored in the memory406 and executable by the processor 403. It is understood that there maybe other applications that are stored in the memory and are executableby the processor 403 as can be appreciated.

Where any component discussed herein is implemented in the form ofsoftware, any one of a number of programming languages may be employedsuch as, for example, C, C++, C#, Objective C, Java®, JavaScript®, Perl,PHP, Visual Basic®, Python®, Ruby, Delphi®, Flash®, or other programminglanguages. A number of software components are stored in the memory andare executable by the processor 403. In this respect, the term“executable” means a program file that is in a form that can ultimatelybe run by the processor 403. Examples of executable programs may be, forexample, a compiled program that can be translated into machine code ina format that can be loaded into a random access portion of the memory406 and run by the processor 403, source code that may be expressed inproper format such as object code that is capable of being loaded into arandom access portion of the memory 406 and executed by the processor403, or source code that may be interpreted by another executableprogram to generate instructions in a random access portion of thememory 406 to be executed by the processor 403, etc. An executableprogram may be stored in any portion or component of the memoryincluding, for example, random access memory (RAM), read-only memory(ROM), hard drive, solid-state drive, USB flash drive, memory card,optical disc such as compact disc (CD) or digital versatile disc (DVD),floppy disk, magnetic tape, or other memory components.

The memory 406 is defined herein as including both volatile andnonvolatile memory and data storage components. Volatile components arethose that do not retain data values upon loss of power. Nonvolatilecomponents are those that retain data upon a loss of power. Thus, thememory 406 may comprise, for example, random access memory (RAM),read-only memory (ROM), hard disk drives, solid-state drives, USB flashdrives, memory cards accessed via a memory card reader, floppy disksaccessed via an associated floppy disk drive, optical discs accessed viaan optical disc drive, magnetic tapes accessed via an appropriate tapedrive, and/or other memory components, or a combination of any two ormore of these memory components. In addition, the RAM may comprise, forexample, static random access memory (SRAM), dynamic random accessmemory (DRAM), or magnetic random access memory (MRAM) and other suchdevices. The ROM may comprise, for example, a programmable read-onlymemory (PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or otherlike memory device.

Also, the processor 403 may represent multiple processors 403 and thememory 406 may represent multiple memories 406 that operate in parallelprocessing circuits, respectively. In such a case, the local interface409 may be an appropriate network that facilitates communication betweenany two of the multiple processors 403, between any processor 403 andany of the memories 406, or between any two of the memories 406, etc.The processor 403 may be of electrical or of some other availableconstruction.

Although portions of the image acquisition application 412, imageprocessing application 415, and other various systems described hereinmay be embodied in software or code executed by general purposehardware, as an alternative the same may also be embodied in dedicatedhardware or a combination of software/general purpose hardware anddedicated hardware. If embodied in dedicated hardware, each can beimplemented as a circuit or state machine that employs any one of or acombination of a number of technologies. These technologies may include,but are not limited to, discrete logic circuits having logic gates forimplementing various logic functions upon an application of one or moredata signals, application specific integrated circuits havingappropriate logic gates, or other components, etc. Such technologies aregenerally well known by those skilled in the art and, consequently, arenot described in detail herein.

The image acquisition application 412 and image processing application415 can comprise program instructions to implement logical function(s)and/or operations of the system. The program instructions may beembodied in the form of source code that comprises human-readablestatements written in a programming language or machine code thatcomprises numerical instructions recognizable by a suitable executionsystem such as a processor in a computer system or other system. Themachine code may be converted from the source code, etc. If embodied inhardware, each block may represent a circuit or a number ofinterconnected circuits to implement the specified logical function(s).

Although the flowchart of FIGS. 1-3 show a specific order of execution,it is understood that the order of execution may differ from that whichis depicted. For example, the order of execution of two or more blocksmay be scrambled relative to the order shown. Also, two or more blocksshown in succession in FIGS. 1-3 may be executed concurrently or withpartial concurrence. Further, in some embodiments, one or more of theblocks shown in FIGS. 1-3 may be skipped or omitted (in favor, e.g.,measured travel times). In addition, any number of counters, statevariables, warning semaphores, or messages might be added to the logicalflow described herein, for purposes of enhanced utility, accounting,performance measurement, or providing troubleshooting aids, etc. It isunderstood that all such variations are within the scope of the presentdisclosure.

Also, any logic or application described herein, including the imageacquisition application 412 and image processing application 415 thatcomprises software or code can be embodied in any non-transitorycomputer-readable medium for use by or in connection with an instructionexecution system such as, for example, a processor 403 in a computersystem or other system. In this sense, the logic may comprise, forexample, statements including instructions and declarations that can befetched from the computer-readable medium and executed by theinstruction execution system. In the context of the present disclosure,a “computer-readable medium” can be any medium that can contain, store,or maintain the logic or application described herein for use by or inconnection with the instruction execution system.

The computer-readable medium can comprise any one of many physical mediasuch as, for example, magnetic, optical, or semiconductor media. Morespecific examples of a suitable computer-readable medium would include,but are not limited to, magnetic tapes, magnetic floppy diskettes,magnetic hard drives, memory cards, solid-state drives, USB flashdrives, or optical discs. Also, the computer-readable medium may be arandom access memory (RAM) including, for example, static random accessmemory (SRAM) and dynamic random access memory (DRAM), or magneticrandom access memory (MRAM). In addition, the computer-readable mediummay be a read-only memory (ROM), a programmable read-only memory (PROM),an erasable programmable read-only memory (EPROM), an electricallyerasable programmable read-only memory (EEPROM), or other type of memorydevice.

In an embodiment, among others, a method for obtaining athree-dimensional volume image of a sample is provided includingobtaining, by dark-field microscopy, a plurality of two-dimensionalimages from the sample, the plurality of images comprising at least onetwo-dimensional image taken at each of a plurality of equally spacedsample locations along a direction of focus, each two-dimensional imagecontaining both in-focus and out-of-focus light from the sample;inputting the plurality of two-dimensional images to a three-dimensionalcomputational method for determining a location of at least onestructure present in the sample; and formulating a three-dimensionalvolume image of the sample, the three-dimensional image showing thelocation of the at least one structure present in the sample.

In any one or more embodiments, the sample can comprise at least onecell and/or other similar biological or nonbiological structure; and atleast one unstained subject particle within the at least one cell. Thedark-field microscopy can use broadband light for illumination of thesample. The at least one cell and/or other similar biological ornonbiological structure can be fluorescent; the at least one unstainedsubject particle can be non-fluorescent; and/or the dark-fieldmicroscopy can comprise broadband light combined with specificwavelengths of fluorescence excitation light for illumination of thesample. The at least one structure present in the sample can comprise:at least one cell and/or other similar biological or nonbiologicalstructure; at least one labeled cell structure within the at least onecell and/or other similar biological or nonbiological structure; and/orat least one non-fluorescent subject particle within the at least onecell and/or other similar biological or nonbiological structure. Thedark-field microscopy can comprise a mixture of narrowband light andbroadband light to illuminate the sample, the narrowband light excitingthe at least one labeled cell structure and the wideband lightscattering from the at least one non-fluorescent subject particle.

In any one or more embodiments, the method can comprise adjusting therelative strengths of the narrowband light and the wideband light toequalize the brightness of the at least one labeled cell structure andthe at least one subject particle in the image, the at least one labeledcell structure contributing weakly and the at least one subject particlecontributing strongly to the image and/or showing the location of atleast one labeled cell structure and the location of the at least onesubject particle in the three-dimensional image without saturation. Inany one or more embodiments, the method can comprise generating themixture of narrowband and wideband light through use of an arc lamp thatcontains at least one peak in its spectral output, light intensity ofthe arc lamp being strong over a narrow wavelength range defined by theat least one peak and weak over wide wavelength ranges outside thenarrow wavelength range defined by the at least one peak; passing lightfrom the arc lamp through an excitation filter with a pass band thatpasses the wavelength of the at least one peak; illuminating the atleast one labeled cell structure with narrowband light passed throughthe excitation filter; illuminating the at least one subject particlesimultaneously with broadband light from the arc lamp; receiving lightthrough a first pass band of an emission filter, the first pass bandpassing light emitted from the at least one labeled cell structure andnot light within the pass band of the excitation filter; and/orreceiving light through a second pass band of the emission filter, thesecond pass band of the emission filter not passing light within thefirst pass band of the emission filter.

In another embodiment, a method for determining a three-dimensional (3D)location of at least one subject particle within a sample is providedincluding obtaining at least two images of the sample by dark-fieldmicroscopy, each of the two images being taken at a different samplelocation along a direction of focus; analyzing the at least two imagesvia 3D deconvolution, wherein the analyzing comprises use of at leastone multiple-point spread function (multiple-PSF); determining thelocation of the at least one subject particle from the result of theanalyzing the at least two images via 3D convolution; and obtaining oneor more 3D images, the one or more 3D images showing the 3D location ofthe at least one subject particle.

In any one or more embodiments, the dark-field microscopy can comprisebroadband light. The at least one multiple-PSF can comprise a spectrallyweighted integral of a plurality of narrowband PSFs over a wavelengthrange. The plurality of narrowband PSFs can comprise at least onecomputed narrowband PSF and/or at least one measured narrowband PSF.

In any one or more embodiments, the method can comprise blurring the atleast one 3D image; interpolating the at least one 3D image in thedirection of focus; and/or locating peaks within the at least one 3Dimage. The at least one multiple-PSF can comprise a subject particlevoxel PSF and a separate non-subject particle voxel PSF. The at leastone subject particle can be coated, the coating changing an opticalspectrum of the at least one subject particle. The sample can be abiological sample. The biological sample can be unstained or stained.The sample can comprise semi-transparent material and/or a fiber matrix.

In another embodiment, a method for determining at least one spatialcorrelation between a location of at least one subject particle and alocation of at least one cell structure within a cell and/or othersimilar biological or nonbiological structure is provided includingdetermining the location of the at least one subject particle within athree-dimensional coordinate system; determining the location of the atleast one cell structure within the three-dimensional coordinate system;formulating a vector description of the location of the at least onesubject particle with respect to the location of the at least one cellstructure in the three-dimensional coordinate system; and determiningthe spatial correlation from the vector description.

In any one or more embodiments, the method can comprise determining thelocation of the at least one subject particle with respect to a locationof an intracellular space within the cell and/or other similarbiological or nonbiological structure and/or determining the location ofthe at least one subject particle with respect to a location of anextracellular space outside the cell and/or other similar biological ornonbiological structure. In any one or more embodiments, the method cancomprise obtaining a 3D density function enclosed by a boundary of theat least one cell structure, the 3D density function describing theplurality of subject particles. In any one or more embodiments, themethod can comprise determining the location of the at least one subjectparticle with respect to a location of an intra-organelle space and/ordetermining the location of the at least one subject particle withrespect to a location of an extra-organelle space.

In any one or more embodiments, the at least one subject particlecomprises a plurality of subject particles. The at least one cellstructure can comprise an organelle. The at least one cell structure cancomprise the entire cell and/or other similar biological ornonbiological structure. The at least one cell structure can be stainedor unstained. In any one or more embodiments, the method can comprisedetermining a minimum distance between the locations of the plurality ofsubject particles and the location of the at least one cell structure.The at least one cell structure can be a stained boundary of the celland/or other similar biological or nonbiological structure. The at leastone cell structure can be a stained nuclear membrane.

In another embodiment, a method of displaying a location of at least onesubject particle is provided including obtaining at least two images ofa sample by dark-field microscopy, each of the at least two images beingtaken at a different sample location along a direction of focus;analyzing the at least two images via 3D deconvolution, wherein theanalyzing comprises use of at least one multiple-point spread function(multiple-PSF); determining the location of the at least one subjectparticle from the result of the analyzing the at least two images via 3Dconvolution; and obtaining one or more 3D images, the one or more 3Dimages showing the 3D location of the at least one subject particle bydisplaying a spherical icon at the 3D location of the at least onesubject particle, the spherical icon representing a unique 3D spatialcoordinate. In any one or more embodiments, the method can comprisedisplaying within the one or more 3D images a semi-transparent volumeimage of cell structure, the 3D location of the at least one subjectparticle being displayed inside the semi-transparent volume image ofcell structure.

In another embodiment, a method for increasing the dynamic range of athree-dimensional image acquired from samples containing weak and strongsources of light is provided including obtaining, by dark-fieldmicroscopy, a short-exposure three-dimensional image from a sample, theshort-exposure image being obtained through use a short exposure time;obtaining, by dark-field microscopy, a long-exposure three-dimensionalimage from the sample, the long-exposure image being obtained throughuse a long exposure time; identifying saturated pixels in thelong-exposure image; excising the identified saturated pixels from thelong-exposure image; replacing the excised pixels in the long-exposureimage with corresponding pixels from the short-exposure image to form afinal image; and rescaling the final image to reflect a common exposuretime. In another embodiment, a method for sharpening a 3D image in avertical direction is provided including processing a vertical profileof each transverse pixel to identify local minima and replacing portionsof the profiles between the local minima with new profile portions thatinclude zeros except for a single value equal to the integral of thatportion, where the single value is located at the center of mass, in thevertical direction, of that portion.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

Therefore, at least the following is claimed:
 1. A method fordetermining a three-dimensional (3D) location of at least one subjectparticle within a sample, the method comprising: obtaining at least twoimages of the sample by dark-field microscopy, each of the two imagesbeing taken at a different sample location along a direction of focus;analyzing the at least two images via 3D deconvolution, wherein theanalyzing comprises use of at least one multiple-point spread function(multiple-PSF), wherein the at least one multiple-PSF comprises aspectrally weighted integral of a plurality of narrowband PSFs over awavelength range; determining the 3D location of the at least onesubject particle from the result of the analyzing the at least twoimages via 3D convolution; and obtaining one or more 3D images, the oneor more 3D images showing the 3D location of the at least one subjectparticle.
 2. The method of claim 1, wherein the dark-field microscopycomprises broadband light.
 3. The method of claim 1, wherein theplurality of narrowband PSFs comprises at least one computed narrowbandPSF.
 4. The method of claim 1, wherein the plurality of narrowband PSFscomprises at least one measured narrowband PSF.
 5. The method of claim1, wherein the sample is a biological sample that is stained orunstained.
 6. The method of claim 1, wherein the sample comprisessemi-transparent material or a fiber matrix.
 7. The method of claim 1,wherein the at least one subject particle has been coated, the coatingchanging an optical spectrum of the at least one subject particle.
 8. Amethod for determining a three-dimensional (3D) location of at least onesubject particle within a sample, the method comprising: obtaining atleast two images of the sample by dark-field microscopy, each of the atleast two images being taken at a different sample location along adirection of focus; analyzing the at least two images via 3Ddeconvolution, wherein the analyzing comprises use of at least onemultiple-point spread function (multiple-PSF); determining the 3Dlocation of the at least one subject particle from the result of theanalyzing the at least two images via 3D convolution; and obtaining oneor more 3D images, the one or more 3D images showing the 3D location ofthe at least one subject particle; blurring at least one 3D image of theone or more 3D images; interpolating the at least one 3D image in thedirection of focus; and locating peaks within the at least one 3D image.9. The method of claim 8, wherein the at least one multiple-PSFcomprises a subject particle voxel PSF and a separate non-subjectparticle voxel PSF.
 10. The method of claim 8, wherein the at least onesubject particle has been coated, the coating changing an opticalspectrum of the at least one subject particle.
 11. The method of claim8, wherein the sample is a biological sample.
 12. The method of claim11, wherein the biological sample is unstained or stained.
 13. Themethod of claim 8, wherein the sample comprises semi-transparentmaterial or a fiber matrix.
 14. The method of claim 8, wherein thedark-field microscopy comprises broadband light.
 15. A method ofdisplaying a location of at least one subject particle, the methodcomprising: obtaining at least two images of a sample by dark-fieldmicroscopy, each of the at least two images being taken at a differentsample location along a direction of focus; analyzing the at least twoimages via three-dimensional (3D) deconvolution, wherein the analyzingcomprises use of at least one multiple-point spread function(multiple-PSF); determining the location of the at least one subjectparticle from the result of the analyzing the at least two images via 3Dconvolution; obtaining one or more 3D images, the one or more 3D imagesshowing a 3D location of the at least one subject particle by displayinga spherical icon at the 3D location of the at least one subjectparticle, the spherical icon representing a unique 3D spatialcoordinate; and displaying within the one or more 3D images asemi-transparent volume image of cell structure, the 3D location of theat least one subject particle being displayed inside thesemi-transparent volume image of cell structure.
 16. The method of claim15, wherein the dark-field microscopy uses broadband light forillumination of the sample.
 17. The method of claim 15, wherein thedark-field microscopy comprises broadband light combined with specificwavelengths of fluorescence excitation light for illumination of thesample.
 18. The method of claim 17, wherein the at least one subjectparticle is non-fluorescent, and the sample comprises at least one cell,biological cellular material or tissue, nonbiological fiber matrix,filter matrix or emulsion that is fluorescent.
 19. The method of claim15, wherein the dark-field microscopy comprises a mixture of narrowbandlight and broadband light to illuminate the sample, the narrowband lightexciting at least one fluorescent structure present in the sample andthe wideband light scattering from the at least one subject particle.20. The method of claim 19, where in the mixture of narrowband light andbroadband light is generated through use of an arc lamp that contains atleast one peak in its spectral output.